https://jfmp.sbu.ac.ir/ Financial Management Perspective en daily 1 Tue, 21 Apr 2026 00:00:00 +0330 Tue, 21 Apr 2026 00:00:00 +0330 https://jfmp.sbu.ac.ir/article_106955.html Introduction: This study aims to develop and evaluate a novel portfolio optimization framework for enhanced index tracking. Enhanced index tracking is an intermediate strategy between active and passive portfolio management, in which the goal is to construct a portfolio from the constituents of a benchmark index so as to closely follow the index while achieving returns above the benchmark. The main objective is to jointly pursue “return enhancement” and “strict control of tail risk” in financial markets where returns may exhibit skewness, excess kurtosis, and extreme events. In such environments, relying solely on conventional variance-based risk measures may underestimate downside risks and thereby expose portfolio-weighting decisions to substantial losses. The proposed framework employs the Omega ratio as a distribution-based performance measure and Conditional Value at Risk (CVaR) as a downside risk control metric, with the aim of generating excess returns over the benchmark while enhancing the portfolio’s resilience to severe losses.Methods: The proposed framework is formulated as an Omega–CVaR optimization problem. The objective function maximizes the Omega ratio of the tracking portfolio in order to improve the ratio of returns above a specified threshold to losses below that threshold. Simultaneously, CVaR is imposed as a constraint to control downside risk by limiting the mean of large losses in the tail of the return distribution. Operational constraints include full investment, minimum/maximum weight bounds of 0% and 50% to prevent excessive concentration, and asset selection restricted to the constituents of the benchmark index. The empirical assessment is conducted using 30 rolling time windows; in each iteration, 52 weeks of in-sample data are used for estimation and 12 weeks of out-of-sample data are used for performance evaluation. The study period spans approximately eight years (from late January 2018 to late December 2025), and the results are compared with those of the Tehran Exchange Price Index (TEPIX) and a competing model based on conventional constraints/objectives.Finding: The results indicate that the Omega–CVaR framework delivers a substantial long-term advantage in terms of cumulative returns relative to both the TEPIX and the competing model. The cumulative return of the proposed portfolio is reported at 2,546%, compared with 1,350% for the TEPIX. Analyzing the time path of performance across rolling windows shows that the primary advantage of the model does not necessarily stem from “persistent weekly outperformance,” but rather from two complementary mechanisms. First, the CVaR constraint, by limiting the average losses beyond the confidence level, reduces the depth of drawdowns during bearish phases. Second, maximizing the Omega ratio leads to a weight allocation that increases the share of desirable returns relative to undesirable losses, enabling the portfolio to better exploit the “compounding effect” during recovery periods following downturns. Nonetheless, statistical tests reveal that at weekly horizons, the model’s outperformance relative to the TEPIX and competing models is not consistently and significantly confirmed. This pattern is consistent with the defensive nature of the portfolio, as evidenced by an average beta of about 0.43, indicating lower sensitivity to market fluctuations than the TEPIX.Conclusions: The findings suggest that the Omega–CVaR framework is an effective tool for enhanced index tracking in volatile markets characterized by extreme risks. Although statistically significant short-term outperformance is not observed, effective control of severe losses and reinforcement of the compounding effect can ultimately lead to higher cumulative returns over the long run. This strategy is particularly recommended for investors seeking lower risk and greater stability in their portfolios, especially in markets with non-normal return distributions. https://jfmp.sbu.ac.ir/article_107047.html Introduction: Behavioral biases of investment fund managers can have significant negative impacts on fund performance, and to reduce these impacts, fund managers can use advanced analytical tools, structured decision-making processes, and training related to financial psychology. Also, investors can choose more efficient and rationally managed funds by being aware of these aspects. These assessments not only help investors in selecting appropriate funds, but also help managers in improving performance and reducing the negative effects of biased behaviors. Identifying behavioral biases of investment fund managers can also help investors in selecting appropriate funds and reducing the risks arising from incorrect decisions. This issue requires more extensive research and the use of new analytical methods, and in this regard, the present study was conducted with the aim of identifying behavioral biases affecting fund performance in investment fund managers. Methods: The present study is of fundamental and exploratory purpose and the data collection stage is of the library-field type with a qualitative method with a content analysis approach. The participants in this study were 20 experts from investment funds who were selected with a purposeful method based on the conditions of expertise. The resources used in the library section included specialized books in the field of finance and behavioral finance, scientific-research articles published in reputable domestic and foreign journals, academic theses and dissertations, official reports and electronic resources and reputable scientific databases. In the field section, the behavioral biases of managers were identified and localized using the Delphi method and the participation of a panel of experts. The content analysis method and the implementation of the Delphi approach were also used to analyze the data. Also, in order to ensure the reliability and stability of coding, multiple coders were used and the level of agreement between them was calculated. Results and discussion: After the initial identification of 80 behavioral trends in the form of 12 main criteria, the collected data were presented to the selected experts of the 80 behavioral trends identified, 54 trends were able to achieve the initial consensus criterion (mean ≥ 3.5). In the second round, the results and average opinions of the first round were given feedback to the experts. They were then asked to evaluate the trends again. At this stage, 68 behavioral trends were able to achieve the consensus criterion and only 12 trends remained outside the consensus range. In the third round, the focus was on final consolidation of consensus and elimination of ambiguous cases. The results showed that at this stage, 72 behavioral biases were agreed upon by experts in the form of 12 final criteria, which include cognitive biases, emotional biases, personality biases, market biases, risk biases, experiential biases, communication biases, time biases, strategic biases, organizational biases, ethical biases and reporting and informational biases. Conclusions: While confirming the prominent role of behavioral factors in the performance of investment funds, the present study highlights the importance of a combined view of managers' decisions; meaning that combining financial engineering tools with behavioral analysis can reduce the gap between actual decisions and optimal decisions. Thus, this study can provide fund managers, investors, regulatory institutions, and financial policymakers with valuable guidance to improve the decision-making process and enhance the performance of investment funds. https://jfmp.sbu.ac.ir/article_107207.html Emerging financial markets are characterized by asymmetric volatility, heavy-tailed return distributions, and nonlinear dependence structures, which pose significant challenges for risk measurement and portfolio optimization. Traditional mean–variance frameworks based on normality assumptions often fail to adequately capture the true nature of financial risk, particularly during periods of market distress and extreme fluctuations. Consequently, portfolio allocation decisions based solely on variance-based measures may underestimate downside risk and lead to suboptimal investment outcomes. This study proposes an empirical framework for portfolio optimization based on Conditional Value-at-Risk (CVaR) and provides a comparative evaluation of standard and robust optimization approaches in the context of the Iranian stock market. The analysis is conducted using daily returns of ten major industry sectors listed on the Tehran Stock Exchange over the period 2015–2025.In the first stage, GJR-GARCH models are employed to capture conditional heteroskedasticity, volatility clustering, and leverage effects and to extract standardized residuals. The empirical results confirm the existence of asymmetric volatility dynamics across most industries, indicating that negative shocks exert a stronger impact on volatility than positive shocks of similar magnitude. Subsequently, Extreme Value Theory (EVT) is applied to model extreme downside risks. The EVT estimates reveal substantial heterogeneity in tail risk across industries, suggesting that exposure to severe losses differs considerably among sectors and highlighting the limitations of conventional risk measures based on normality assumptions.To model nonlinear dependence among industry returns, Archimedean copulas, including Clayton, Frank, and Gumbel specifications, are estimated. The results indicate that dependence structures among industries are not purely linear and that tail dependence plays an important role in the transmission of market risk. Based on the estimated copula structures, joint return scenarios are generated and incorporated into two portfolio optimization frameworks: Mean–CVaR and Robust-CVaR.The optimization results show that the robust framework produces a more balanced allocation of portfolio weights and a higher degree of diversification than the classical Mean–CVaR approach. While the conventional model tends to concentrate portfolio weights in a limited number of sectors, the robust framework reduces sensitivity to estimation errors and parameter uncertainty. In-sample evidence further indicates improvements in risk-adjusted performance under the Robust-CVaR specification.To assess the robustness and generalizability of the findings, an out-of-sample evaluation based on six expanding rolling windows covering the period 2020–2025 is conducted. The results demonstrate that the robust portfolio performs similarly to or better than the classical portfolio in most evaluation periods and consistently achieves higher Sharpe ratios under the Clayton copula specification. Moreover, the rolling-window analysis reveals that copula dependence parameters vary over time, providing empirical evidence of parameter instability and uncertainty in the portfolio construction process.Overall, the findings suggest that integrating GJR-GARCH, Extreme Value Theory, Archimedean copulas, and robust CVaR optimization provides a coherent framework for risk measurement, dependence modeling, and portfolio construction in emerging financial markets. The results also underscore the importance of accounting for asymmetric dependence structures and parameter uncertainty in asset allocation decisions and demonstrate that robust portfolio optimization can improve portfolio stability while mitigating the adverse effects of estimation errors. https://jfmp.sbu.ac.ir/article_107208.html AbstractPurpose: Corporate sustainability reporting, which involves disclosing the outcomes of environmental, social, and governance (ESG) activities, has become increasingly important over the past two decades. Consequently, studying the causes and financial consequences of corporate sustainability reporting has also grown significantly within the fields of finance and accounting. The disclosure of this information, which is generally non-financial, has influenced corporate characteristics and investor decision-making, thereby becoming an interdisciplinary research area. The aim of this study is to investigate the relationship between sustainability (activities) disclosure and corporate financial flexibility, with an emphasis on the moderating effect of economic policy uncertainty.Methodology: The target statistical population was selected under certain conditions for statistical analysis, consisting of 101 companies over the period from 2013 to 2024 (1392–1403 in the Persian calendar), totaling 1,212 firm-year observations. Multiple regression models were used to test the hypotheses. The models were estimated using a cross-sectional fixed-effects approach as the primary method, and a complementary approach using year- and industry-fixed effects with cluster correction (to mitigate the effects of heteroscedasticity and autocorrelation on the calculation of regression coefficients) was employed as a robustness check.Findings: The results of the statistical analyses of the hypotheses showed that sustainability disclosure (ESG) has a direct and significant relationship with the level of corporate financial flexibility. Furthermore, the findings confirmed the significant moderating effect of economic policy uncertainty on the relationship between sustainability disclosure and financial flexibility, such that economic policy uncertainty weakens the relationship between sustainability disclosure and the level of financial flexibility. The findings from additional tests also support the main results and confirm the robustness of the findings.Conclusion: The findings of this study, while confirming a positive and significant relationship between sustainability disclosure (ESG) and financial flexibility, show that economic policy uncertainty significantly weakens this relationship. This result is of double significance from a theoretical perspective. On the one hand, it supports signaling theory and instrumental stakeholder theory, according to which the disclosure of sustainability information increases a firm's financial resources by reducing information asymmetry and gaining stakeholder trust. On the other hand, the weakening effect of economic policy uncertainty aligns with real options theory, which predicts that under conditions of high uncertainty, firms tend to postpone investments in long-term projects, including sustainability activities, and preserve their liquidity. From a practical perspective, these results warn corporate managers that during periods of high economic uncertainty, relying solely on sustainability disclosure to improve financial flexibility is insufficient, and complementary strategies such as increasing cash reserves and diversifying financing sources must also be pursued. Furthermore, for investors and financial analysts, this finding indicates that under conditions of economic recession or instability, strong ESG performance alone cannot guarantee a firm's financial flexibility, and simultaneous assessment of environmental uncertainty indicators is essential. At the policy level, governments and regulatory bodies should be aware that promoting sustainability disclosure during periods of economic instability, without concurrently reducing policy uncertainty, may not lead to desirable outcomes. Thus, by revealing the weakening role of economic policy uncertainty, this study expands the existing knowledge frontiers in corporate sustainability and opens new avenues for future research on the interaction between macroeconomic risks and firm-level strategies. https://jfmp.sbu.ac.ir/article_107259.html Purpose: This study aims to propose and evaluate a dynamic framework for portfolio optimization in cryptocurrency markets that ensures an effective balance between return maximization and risk control under conditions of high volatility. The primary objective is to develop a risk-sensitive adaptive agent that integrates multiple baseline deep reinforcement learning agents with a dynamic switching mechanism to adjust asset weight allocations over time. This adaptive structure enables conservative behavior during highly volatile market regimes and more aggressive positioning during trending periods. The empirical analysis is conducted on a selected set of five major cryptocurrencies—Bitcoin, Ethereum, Solana, Ripple, and Tether—to assess the effectiveness and practical applicability of the proposed framework in real-world market environments.Method: Daily data spanning five years were collected from Yahoo Finance and, after data cleaning, were divided into training and testing sets using a 70%–30% split. The state representation comprised asset prices, trading volumes, and commonly used technical indicators. To capture long-term temporal dependencies, state encoding was implemented using a Transformer-based architecture. Two categories of methods were examined: (1) individual deep reinforcement learning agents—namely PPO, A2C, and DQN—each independently managing the portfolio; and (2) a risk-sensitive adaptive framework that dynamically switches among agents by monitoring short-term performance indicators such as rolling cumulative profit and loss and five-day moving volatility. The reward function incorporated risk-sensitive components, including a logarithmic utility of returns, a volatility penalty, and the Calmar ratio. Performance evaluation was conducted using cumulative return, annualized return, Sharpe ratio, Sortino ratio, Calmar ratio, and maximum drawdown.Findings: On the out-of-sample test dataset, the proposed risk-sensitive adaptive framework consistently outperformed each individual deep reinforcement learning agent. During non-trending market periods, the proposed model achieved higher terminal portfolio value and superior risk-adjusted performance compared to alternative methods. For instance, it recorded a Sharpe ratio of approximately 1.13 and a Sortino ratio of about 1.81. In bullish market regimes, the framework delivered markedly stronger results, achieving a Sharpe ratio of approximately 1.73, a Calmar ratio of around 3.34, and higher annualized returns, such that the final portfolio value during the uptrend was nearly twice that of the best-performing standalone agent. The quantitative results indicate that dynamic switching among aggressive, balanced, and conservative agents enhances the exploitation of favorable market trends while mitigating losses during highly volatile periods. Although a temporary increase in maximum drawdown was observed in certain scenarios, this effect was effectively offset by a reversion mechanism that shifts the agent back toward conservative behavior.Conclusion: Integrating a Transformer architecture for temporal feature extraction with a risk-sensitive adaptive agent framework provides an effective solution for dynamic portfolio management in cryptocurrency markets, particularly when the objective is to simultaneously enhance returns and control downside risk. The findings indicate that employing a dynamic switching mechanism among multiple deep reinforcement learning agents can significantly improve portfolio performance and stability when facing shifting market regimes. Furthermore, statistical significance analysis using the Wilcoxon Signed-Rank Test across ten independent random seeds confirmed that the proposed model significantly outperformed PPO, A2C, and DQN in all major performance metrics (p < 0.01), demonstrating both robustness and reproducibility of the results. https://jfmp.sbu.ac.ir/article_107260.html Introduction: Understanding the dynamic interdependence among industries within stock market indices is crucial for investment decision-making and the design of economic policies. The Pearson correlation coefficient only captures linear dependence. However, the return distribution of financial variables is not elliptical. Moreover, extreme events occurring in the tails of the distribution during crisis periods can rapidly propagate across financial markets, leading to stronger interdependence, which necessitates the use of more advanced and sophisticated models. It is noteworthy that copula-based models enable the modeling of nonlinear dependencies using flexible choices of marginal distributions. Among the various families of copulas, vine copulas allow for flexible modeling of complex dependence structures by utilizing a wide class of bivariate copulas. The interdependence of individual stock returns is depends on the dependence between different sectors of the stock market. Therefore, it is essential to understand tail dependence across sectors, as each sector often responds differently to economic circumstances. The aim of this study is to reveal the dependence structure of 70 stocks across 10 industries using C-vine, R-vine, and D-vine models.Methods: In this paper, a ARMA-EGARCH (1,1) model with Student-t innovations is employed for the marginal distributions. To define copula data, the cumulative distribution function (CDF) corresponding to the Student-t distribution is employed as a probability integral transform. Next, copula functions are selected using the marginal data, and vine structures are developed. In this paper, parameters are estimated using the sequential estimation (SE) method and the joint maximum likelihood estimation (MLE).Results and discussion: The root nodes or industry representatives in the R-vine trees are as follows: Seshahed represents the real estate development industry; Vaomid serves as the representative of the industrial conglomerates sector, while Desobha represents the chemicals and pharmaceutical products sector; Kegol represents the metal ore extraction industry, while Femeli is considered the representative of the basic metals industry; Automobile and auto parts companies are represented by Khazin, while Beterans serves as the representative of companies active in the machinery and electrical equipment industry. The investment industry, the chemical products industry, and the cement, lime, and plaster industry are represented by Pardis, Sharak, and Ceshomal, respectively. The dependence among industry representatives in the R-vine specification is extracted. Furthermore, Node 1 plays a central role among various firms in the chemicals and pharmaceutical products, basic metals, automotive and auto parts, and investment industries. The results also confirm that R-vine models are preferred over D-vine and C-vine models.Conclusions: According to the results, the first hypothesis—“The dependency structure among industries listed on the Tehran Stock Exchange does not follow a symmetric (normal) pattern and exhibits tail dependence”—is supported. The dependency structure among the ten industry representatives is modeled using various copula families, including the BB8 and survival BB8 copulas. According to the findings, the second hypothesis—“The interconnections among industries listed on the Tehran Stock Exchange possess a multilayered and network-based nature and cannot be reduced to a purely centralized (C-vine) or chain-like (D-vine) structure”—is also supported. The empirical superiority of the R-vine model confirms the hypothesis of networked and multi-source interdependencies within the Tehran Stock Exchange, which is consistent with the structural characteristics of the Iranian economy. In emerging markets such as the Tehran Stock Exchange, dependencies often arise from multiple sources of risk (e.g., exchange rate fluctuations, commodity price shocks, and monetary policy). The economic structure is diversified yet imbalanced, and shocks may propagate simultaneously through multiple transmission channels. R-vine models exhibit superior explanatory power and goodness of fit in modeling inter-industry dependencies compared to C-vine and D-vine structures. In the context of the Tehran Stock Exchange—where dependencies are heterogeneous, shock transmissions occur with varying intensities, and ownership structures follow distinctive patterns—the R-vine framework provides a more flexible and better-fitting representation of inter-industry dependence dynamics.From an economic perspective, the superiority of the R vine model suggests that shock transmission in the Tehran Stock Exchange does not occur through a single pathway or via a dominant industry. Rather, different industries—depending on their positions within the dependency network—play distinct roles in either absorbing or transmitting risk. This finding is consistent with the structure of the Iranian economy, which is simultaneously influenced by factors such as exchange rate movements, global commodity prices, domestic policies, and institutional constraints. Consequently, the co movement of industries in the Tehran Stock Exchange should be understood as the outcome of interactions among multiple sources of risk, rather than merely a response to a single common factor.